Determinants of Artificial Intelligence Adoption in Student Support Information Systems: Evidence From Vietnamese Higher Education

Why It Matters

The findings show that universities must prioritize MIS integration and data governance before expecting widespread AI uptake, reshaping investment priorities in higher‑education tech strategy.

Key Takeaways

• MIS quality drives data integrity for AI initiatives
• High data quality boosts AI readiness in higher education
• Perceived AI benefits weakly influence system acceptance
• Direct MIS and data effects outweigh user perception

Pulse Analysis

Artificial intelligence promises to transform student support services, from personalized tutoring to predictive alerts for at‑risk learners. Yet many institutions, especially in emerging markets, struggle with fragmented information systems and inconsistent data, limiting AI's impact. In Vietnam, higher‑education leaders are grappling with these exact challenges, prompting researchers to examine how foundational technology components affect AI rollout. By situating AI adoption within the broader context of digital transformation, the study underscores that without solid MIS infrastructure, even the most sophisticated algorithms cannot deliver value.

The research surveyed 450 respondents across administrative, faculty, and student groups, employing composite scores to mitigate scale reliability issues. Regression‑based path analysis revealed a clear chain: robust MIS enhances data quality, which then elevates AI readiness. Interestingly, while AI readiness improves perceived benefits, it only modestly influences actual acceptance, and perceived benefits alone do not predict adoption. This suggests that institutional readiness and data governance are the primary levers for successful AI integration, outweighing the traditional focus on user attitudes.

For university executives, the practical takeaway is to invest first in system integration and data stewardship before scaling AI solutions. Strengthening MIS platforms, standardizing data collection, and establishing clear governance policies can create the conditions needed for AI to be trusted and adopted. As higher‑education markets become more competitive, institutions that master these foundational steps will likely achieve faster, more sustainable AI-driven improvements in student outcomes, positioning themselves as leaders in the digital campus era.